Extension of Metabolic Control Analysis and Biochemical Systems Theory to Stochastic Systems

代谢控制分析和生化系统理论向随机系统的扩展

• 批准号: 0827592
• 负责人: Herbert Sauro
• 金额: $ 65.99万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0827592&HistoricalAwards=false
• 关键词: Extension; Metabolic; Control; Analysis; Biochemical

The objective of this project is to develop a theory of perturbation analysis for stochastic biochemical reaction systems and to use this theory to uncover the stochastic properties of a variety of biological network motifs. Cellular processes are controlled by networks of interacting proteins, genes and small molecules. Traditionally such systems have been described using a continuum/deterministic model, often based on differential equations. In these models it is assumed that the concentrations of the various molecular species can be described using continuous variables. In a number of cases this is quite reasonable because the number of molecules of any particular species can be large. However there are some cellular subsystems, such as gene networks, where the number of molecules might be as low as a few. In such systems a continuous model is inappropriate. Moreover, because the molecular numbers are so small, the stochastic nature of chemical processes becomes a dominant feature. In this project the deterministic theory will be reformulated in order to develop a general analytical theory of noise propagation in biochemical networks. The project will use the theory to understand the stochastic properties of different network motifs and to make predictions which can be tested experimentally. Biological cells contain millions of molecules randomly colliding and reacting. The net effect of all this activity is noise, called stochastic noise. Electrical engineers have to deal with unwanted noise in electronic circuits all the time and they go to great efforts to eliminate it. Experimentally, noise in cells can be measured but what is lacking is an understanding of how this noise behaves in such complicated systems. If noise could be better understood, engineers would be in a better position to control it. This project will develop a mathematical theory that will allow scientists and engineers to understand and predict how noise spreads inside a cell. This project impinges on many areas of science, including molecular biology, computer science, control theory, signal processing and electrical circuit theory. Undergraduate and graduate students will participate in this project and they will be trained in multidisciplinary science.

这个项目的目标是发展一个随机生化反应系统的扰动分析理论，并利用这个理论来揭示各种生物网络图案的随机特性。细胞过程由相互作用的蛋白质、基因和小分子网络控制。传统上，这样的系统已被描述使用连续/确定性模型，往往基于微分方程。在这些模型中，假设各种分子种类的浓度可以使用连续变量来描述。在许多情况下，这是相当合理的，因为任何特定物种的分子数量都可以很大。然而，有一些细胞子系统，如基因网络，其中分子的数量可能低至几个。在这样的系统中，连续模型是不合适的。此外，由于分子数量如此之小，化学过程的随机性成为一个主要特征。 在本项目中，将重新制定确定性理论，以开发生化网络中噪音传播的通用分析理论。该项目将使用该理论来理解不同网络基序的随机特性，并做出可以通过实验测试的预测。生物细胞包含数百万个随机碰撞和反应的分子。所有这些活动的净效应是噪声，称为随机噪声。电子工程师必须始终处理电子电路中不必要的噪声，并努力消除它。实验上，可以测量细胞中的噪声，但缺乏的是对这种噪声在如此复杂的系统中如何表现的理解。如果能够更好地理解噪音，工程师将能够更好地控制它。该项目将开发一种数学理论，使科学家和工程师能够理解和预测噪音如何在细胞内传播。该项目涉及到许多科学领域，包括分子生物学、计算机科学、控制理论、信号处理和电路理论。本科生和研究生将参加这个项目，他们将在多学科科学培训。